Systems and methods for detecting photoplethysmographic device usage

ABSTRACT

Systems and methods for determining if a wearable photoplethysmography device is correctly positioned in operating to medical signs of a user by using a classifier to determine if a signal is valid or invalid. In some embodiments, in using the classifier to determine in a signal is valid or invalid, a lean method of linear computational complexity and minimal memory complexity is provided for determining at the wearable photoplethysmography device if it is correctly positioned. In some embodiments, in using the classifier minimal computational complexity is used in determining at the wearable photoplethysmography device if it is correctly positioned.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims the benefit of U.S. Provisional PatentApplication Ser. No. 62/262,540, filed Dec. 3, 2015, entitled“Prediction of Blood Pressure from PPG Data,” U.S. Provisional PatentApplication Ser. No. 62/262,532, filed Dec. 3, 2015, entitled “Validityand Classification for Biological Signal Recognition,” and U.S.Provisional Patent Application Ser. No. 62/262,342, filed Dec. 2, 2015,entitled “Respiratory Rate Estimation Using Multispectral Data,” whichare incorporated herein by reference.

BACKGROUND Technical Field

Embodiments of the present inventions relate generally tophotoplethysmography devices. More specifically, embodiments of thepresent inventions relate to validity of measurements made byphotoplethysmography devices.

Description of Related Art

Wearable activity monitoring devices are growing in popularity. Thesedevices are directed to facilitate monitor medical signs of a user toimprove overall health. In particular, minimally invasivephotoplethysmography devices have been developed to measure medicalsigns of users to improve overall health of the user. It is importantthat any such measurements are valid in order to improve overall healthof the users. However, such devices are susceptible to being positionedincorrectly leading to the measuring medical signs being invalid.Further, signs measured by a current wearable activity monitoringdevices are often processed even though the signs are invalid. As aresult, these devices lead to wasted power consumption for no benefit.

Photoplethysmography devices have been implemented in wearable devices.Such devices are small to allow for easy wearing by a user and also lackcomputational resources. Due, to the lack of size and computationalresources, there has been difficulty in implementing systems and methodsat the photoplethysmography devices that allows for thephotoplethysmography devices themselves to determine if they arepositioned correctly.

SUMMARY

An example system may comprise a signal input module at a wearablephotoplethysmography device. The signal input module may be configuredto receive a signal from an energy receiver of the wearablephotoplethysmography device. The signal may be generated by the energyreceiver, at least in part, based upon a received portion of energyprojected by an energy transmitter of the wearable photoplethysmographydevice operating to measure medical signs of a user.

The system may include a wave feature selection module configured toselect a subset of wave features of a plurality of wave features of thesignal received from the energy receiver. The system may comprise a wavemetric calculation determination module configured to determine wavemetric calculations of a set of wave metric calculations for the subsetof wave features selected by the wave feature selection module.Additionally, the system may comprise a wave metric-based signalvalidity classification module configured to classify the signal byapplying a validity classifier to at least one of the wave metriccalculations to generate a validity score for the signal. In variousembodiments, the validity classifier generated based on the set of wavemetric calculations. The validity classifier may be used to determine ifthe wearable photoplethysmography device is correctly positioned inoperating to measure the medical signs of the user.

The system may include a validity prediction module configured tocompare the validity score to a validity threshold to determine if thewearable photoplethysmography device is correctly positioned inoperating to measure the medical signs of the user based on applicationof the validity classifier to the at least one of the wave metriccalculations. The system may include a signal validity-based devicecontrol module at the wearable photoplethysmography device configured tocontrol operation of the wearable photoplethysmography device based onwhether it is determined the wearable photoplethysmography device iscorrectly positioned in operating to measure the medical signs of theuser based on a comparison of the validity score to the validitythreshold.

In some embodiments, the validity prediction module may be configured todetermine the wearable photoplethysmography device is operating tomeasure the medical signs of the user based on the comparison of thevalidity score to the validity threshold.

In some embodiments, the signal validity-based device control module maybe configured to cause the energy transmitter to stop operating inemitting energy for use by the wearable photoplethysmography device inoperating to measure the medical signs of the user.

In some embodiments, the wave metric-based signal validityclassification module may be configured to classify the signal byapplying the validity classifier to the wave metric calculations of theset of wave metric calculations to generate a plurality of validityscores including the validity score for the signal;

In some embodiments, the example system includes a smoothing moduleconfigured to apply temporal smoothing to the plurality of validityscores for the signal to generate a smoothed validity score;

In some embodiments, the validity prediction module may be configured tocompare the smoothed validity score to the to the validity threshold todetermine if the wearable photoplethysmography device is correctlypositioned in operating to measure the medical signs of the user;

In some embodiments, the signal validity-based device control module maybe configured to control operation of the wearable photoplethysmographydevice based on whether it is determined the wearablephotoplethysmography device is correctly positioned in operating tomeasure the medical signs of the user based on a comparison of thesmoothed validity score to the validity threshold.

In some embodiments, the smoothing module may be configured to applyrank smoothing to the plurality of validity scores, the smoothedvalidity score selected from the plurality of validity scores based onthe value of the smoothed validity score according to the ranksmoothing.

In some embodiments, the wave feature selection module may be configuredto normalize the signal before selecting the subset of wave features ofthe plurality of wave features of the signal.

An exemplary method may comprise receiving a signal from an energyreceiver of a wearable photoplethysmography device. The signal may begenerated by the energy receiver, at least in part, based upon areceived portion of energy projected by an energy transmitter of thewearable photoplethysmography device operating to measure medical signsof a user. The method may further comprise selecting a subset of wavefeatures of a plurality of wave features of the signal received from theenergy receiver, determining wave metric calculations of a set of wavemetric calculations for the subset of wave features selected from thesignal, classifying the signal by applying a validity classifier to atleast one of the wave metric calculations to generate a validity scorefor the signal, comparing the validity score to a validity threshold todetermine if the wearable photoplethysmography device is correctlypositioned in operating to measure the medical signs of the user, andcontrolling operation of the wearable photoplethysmography device basedon whether it is determined the wearable photoplethysmography device iscorrectly positioned in operating to measure the medical signs of theuser based on a comparison of the validity score to the validitythreshold.

In some embodiments, the example method may include classifying thesignal by applying the validity classifier to the wave metriccalculations of the set of wave metric calculations to generate aplurality of validity scores including the validity score for thesignal, applying temporal smoothing to the plurality of validity scoresfor the signal to generate a smoothed validity score, comparing thesmoothed validity score to the to the validity threshold to determine ifthe wearable photoplethysmography device is correctly positioned inoperating to measure the medical signs of the user, and controllingoperation of the wearable photoplethysmography device based on whetherit is determined the wearable photoplethysmography device is correctlypositioned in operating to measure the medical signs of the user basedon a comparison of the smoothed validity score to the validitythreshold.

In some embodiments, the example method may include normalizing thesignal before selecting the subset of wave features of the plurality ofwave features of the signal.

In some embodiments, the wave metric calculations of the set of wavemetric calculations to which the validity classifier is applied mayinclude signal energy measurements of the signal determined from thesubset of wave features.

In some embodiments, the wave metric calculations of the set of wavemetric calculations to which the validity classifier is applied mayinclude signal mobility measurements of the signal determined from thesubset of wave features and the signal energy measurements of the signaldetermined from the subset of wave features.

In some embodiments, the wave metric calculations of the set of wavemetric calculations to which the validity classifier is applied mayinclude complexity measurements of the signal determined from the subsetof wave features and the signal mobility of the measurements of thesignal determined from the subset of wave features.

In some embodiments, the wave metric calculations of the set of wavemetric calculation to which the validity classifier is applied mayinclude one or a combination of signal crossing measurements of thesignal and signal non-oscillatory component measurements of the signaldetermined from the subset of wave features.

In some embodiments, the validity classifier applied to the wave metriccalculations may be generated from one or a combination of signal energymeasurements, signal mobility measurements, signal complexitymeasurements, signal crossing measurements, and signal non-oscillatorycomponent measurements of signals known to be valid or invalid by beinggenerated by at least one wearable photoplethysmography device correctlypositioned in operating to measure the medical signs and at leastanother wearable photoplethysmography device incorrectly positioned inoperating to measure the medical signs.

In some embodiments, applying temporal smoothing may include applyingrank smoothing to the plurality of validity scores and the smoothedvalidity score selected from the plurality of validity scores may bebased on the value of the smoothed validity score according to the ranksmoothing.

Other features and aspects of various embodiments will become apparentfrom the following detailed description, taken in conjunction with theaccompanying drawings, which illustrate, by way of example, the featuresof such embodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating an example photoplethysmographydevice.

FIG. 2 illustrates an example flow diagram of a method of controlling aphotoplethysmography device based on a determination of whether thephotoplethysmography device is correctly positioned in operating tomeasure medical signs of a user.

FIG. 3 is a system diagram illustrating an example environment utilizinga photoplethysmography device 302 in accordance with variousembodiments.

FIG. 4 illustrates an example photoplethysmography device for measuringvarious medical signs in accordance with embodiments of the presentapplication.

FIG. 5 is a block diagram of an example digital device.

FIG. 6 is a block diagram of an example signal validity classificationsystem.

FIG. 7 depicts an example flow diagram of a method of determiningwhether a photoplethysmography device is positioned correctly byapplying a validity classifier to a signal generated by thephotoplethysmography device in operation.

FIG. 8 is a block diagram of an example wave metric calculationdetermination module.

FIG. 9 depicts an example flow diagram of a method of determining wavemetrics of a set of wave metrics for a subset of wave features.

FIG. 10 is a block diagram of an example validity classifier buildersystem.

FIG. 11 depicts an example flow diagram of a method of generating avalidity classifier for purposes of determining whether a wearablephotoplethysmography device is correctly positioned in operating tomeasure medical signs of a user.

DETAILED DESCRIPTION

Medical signs including blood metrics may be measured by minimallyinvasive procedures to discover diseases, diagnose diseases, or addressmedical conditions. Minimal-invasive procedure based devices may havethe advantages of reducing costs and decreasing the need for invasivemethods, thereby increasing the comfort and well-being of users andpatients. Even though these devices have revolutionized patient care,they have only been approved for medical purposes. Minimal-invasiveprocedure based devices are usually out of reach for the general publicbecause they are designed for medical purposes rather than non-medicalpurposes such as fitness, well-being, and quality of life.

For example, personal devices such as sphygmomanometers or pulseoximeters measure blood pressure or oxygen levels, respectively, on aper-request basis. They usually do not measure blood metrics in realtime or periodically. Real-time blood metrics data (e.g., highresolution measurements or measurements over long periods of time) mayallow users monitoring and controlling their energy levels and/ormetabolism. Nutritionists, people suffering from obesity, peopledesiring to eat healthier, fitness enthusiasts, semi-professionalathletes, people likely to have hypoglycemia, and/or the generalpopulation may benefit from these devices.

In various embodiments, a wearable photoplethysmography device measuresmedical signs of users. The photoplethysmography device may measure anynumber of applicable medical signs including, for example, respirationrates, depth of anesthesia, hypovolemia, and hypervolemia. Thephotoplethysmography device may monitor, store, track, communicate,and/or analyze medical signs of a user.

In various embodiments, the wearable photoplethysmography device may beor include a multispectral blood metric measurement apparatus thatmonitors blood metrics, fitness, and/or metabolism levels of varioususers in a non-invasive manner. The multispectral blood metricmeasurement apparatus may measure any number of blood metrics which mayinclude, for example, various nutrient blood concentrations.

Wearable photoplethysmography devices need to be positioned correctlywith respect to a user to accurately determine the user's medical signs.Incorrectly positioned wearable photoplethysmography devices includesthose wearable photoplethysmography devices that are positionedimproperly on the user (e.g., the wearer) or are not worn at all. Whilean incorrectly positioned wearable photoplethysmography device may stillfunction to generate and detect optical signals, any measurements basedon those detected optical signals will be invalid. For example, if thewearable photoplethysmography device is not positioned on the usercorrectly, the photoplethysmography device may receive optical signalswith too much noise, interference, and/or distortion. Invalid signals(e.g., signals with too much noise, interference, and/or distortion) maylead to erroneous measurements of medical signs. In another example, ifthe user is not wearing the wearable photoplethysmography device at all,any detected signals by the wearable photoplethysmography device will beinvalid for evaluating medical signs of the user.

Invalid signals (e.g., caused by a user not wearing the wearablephotoplethysmography device or not positioning the wearablephotoplethysmography device correctly) lead to a waste by consumingcomputational resources in calculating measurements of medical signsthat are ultimately worthless or otherwise erroneous.

Previous attempts have been made to determine whetherphotoplethysmography devices are correctly positioned to operate inmeasuring medical signs. For examples, sensors have been incorporatedinto photoplethysmography devices for use in determining whether thephotoplethysmography devices are correctly positioned to measure medicalsigns of a user. Incorporating other sensors into photoplethysmographydevices to determine whether the photoplethysmography devices arecorrectly positioned have, in the past, increased the size of theapparatuses and increased the cost of the apparatuses.

A photoplethysmography device may be determined to be correctlypositioned based on energy transmitted between energy transmitter andreceiver of the photoplethysmography device. For example, signals basedon energy transmitted between an energy transmitter and a receiver maybe compared to historical data to detect invalid windows usingphysiological parameter estimates. Such approaches may consumeconsiderable computational resources. The consumption of resources iscritical when the photoplethysmography devices are implemented aswearable devices with limited processing power and data storage space.For example, approaches using comparisons of signals to historical datarequire signal warping and comparisons which may consume considerablecomputational resources. Additionally, such approaches do not accountfor differences between wearers and differences in medical situationsfor which medical signs are determined using a photoplethysmographydevice. For example, approaches using comparisons of signals tohistorical data do not account for differences in characteristicsbetween different users, while approaches using invalid window detectionuses bounds on physiological parameters that are exceeded in certainmedical situations.

FIG. 1 is a block diagram illustrating an example photoplethysmographydevice 100. The photoplethysmography device 100 comprises an energytransmitter 104, an energy receiver 106, a signal validityclassification system 108, and a signal validity-based device controlmodule 110. In various embodiments, the photoplethysmography device 100may be implemented as a wearable member. The wearable member mayinclude, for example, a bracelet, glasses, necklace, ring, anklet, belt,broach, jewelry, clothing, or any other member or combination of membersthat allow the photoplethysmography device 100 to be close to or touch abody of the wearer to measure medical signs. In various embodiments, thephotoplethysmography device 100 may be correctly positioned orincorrectly positioned to measure medical signs of a user. For example,the photoplethysmography device 100 may be not on the user's body at allor placed incorrectly (e.g., askew) such that signal measurement willlead to inaccurate results.

The energy transmitter 104 and the energy receiver 106 may be positionedon the photoplethysmography device 100 such that the energy transmitter104 and the energy receiver 106 make contact or are proximate to tissue(e.g., skin) of a user. The signal validity classification system 108and the signal validity-based device control module 110 may be coupledto the energy transmitter 104 and the energy receiver 106. The signalvalidity classification system 108 may be coupled to the energytransmitter 104 and the energy receiver 106.

The photoplethysmography device 100 may comprise a communication module(not shown) and/or an analyzer (not shown). The analyzer may be coupledto the energy receiver 106, the signal validity classification system108, and/or the signal validity-based device control module 110. Thecommunication module may be coupled to the energy receiver 106, thesignal validity classification system 108, and/or the signalvalidity-based device control module 110.

The photoplethysmography device 100 may further comprise a driver (notshown) and a power source (not shown). The driver may be coupled to oneor a combination of the energy transmitter 104, the energy receiver 106,the signal validity classification system 108, and the signalvalidity-based device control module 110. Similarly, the power source(e.g., a battery, capacitor, or other power supply) may supply power tothe energy transmitter 104, the energy receiver 106, the signal validityclassification system 108, and/or the signal validity-based devicecontrol module 110 via the driver.

In some embodiments, the photoplethysmography device 100 comprises anAnalog-to-Digital Converter (“ADC”) (not shown). The ADC may be coupledto the signal validity classification system 108 and the signalvalidity-based device control module 110.

In various embodiments, the energy transmitter 104 emits energy 112including, but not limited to, light (e.g., optical beam(s) or opticalsignal(s)). For example, the energy transmitter 104 may emit energy 112into the body (e.g., tissues) of the user, when the photoplethysmographydevice 100 is being worn by the user. The energy 112 emitted by theenergy transmitter 104 may be in the direction of the user's tissues.The energy transmitter 104 may emit energy 112 or light at differentwavelengths. The energy transmitter 104 may comprise any number of lightemission diodes (“LEDs”) or other energy sources. While LEDs arediscussed herein for energy transmission, it will be appreciated thatthe energy transmitter 104 may include any number and any kind of energysources (e.g., light sources).

In one example, the energy transmitter 104 comprises at least two LEDs.Each LED may be configured to emit energy 112 at one or morewavelengths. Each LED may emit light with a peak wavelength centeredaround a wavelength. For example, the energy transmitter 104 may emitlight with a peak wavelength centered between 500 nm to 1800 nm.Further, in the example, the energy transmitter 104 may emit differentwavelengths of light centered around at least one of 523 nm, 590 nm, 623nm, 660 nm, 740 nm, 850 nm, and 940 nm. In another example, the energytransmitter 104 may emit light with a wavelength within the infraredspectrum.

Wavelengths of light emitted by the energy transmitter 104 maycorrespond to one or more metrics of medical signs to be measured. Forexample, wavelengths of light emitted by the energy transmitter 104 maycorrespond to one or more blood metrics of interest and/or one or morenutrients. Different components of the blood and/or different nutrientsmay absorb energy at different wavelengths. In various embodiments, acontroller, driver, analyzer, or the like may assess or analyze metricsof medical signs being measured or estimated (e.g., of a user of thephotoplethysmography device 100 and/or a user device not shown). Theanalyzer may determine metrics of medical signs from signals generatedby the energy receiver 106 based on the receive energy 106 (e.g., theenergy receiver 106 may generate a signal when energy is detected orotherwise received).

The controller, driver, analyzer or the like may associate medical signsto be measured with one or more wavelengths and configure one or more ofthe LEDs to emit energy 112 of at least one of the one or morewavelengths. For example, an analyzer may command the driver to deliverelectric power to an LED that is configured to emit light at a desiredwavelength.

In some embodiments, a number of wavelengths generated by the energytransmitter 104 are the number of blood components or molecules to bemeasured plus one. For example, when a total number of five (5) bloodcomponents and/or molecules are to be measured, a total number of six(6) wavelengths may be determined based on the blood components and/ormolecules to be measured. One or more wavelengths may be associated witha nutrient or a combination of nutrients. In some embodiments, a numberof wavelengths generated by the energy transmitter 104 are the number ofnutrients to be measured plus one. For example, when a total number ofthree (3) nutrients are to be measured, a total number of four (4)wavelengths may be determined based on the nutrients to be measured.

The energy transmitter 104 may be configured to generate energy at a setof wavelengths. The energy transmitter 104 may be configured to generateenergy such that energy at different wavelengths is generatedsequentially and/or periodically. A period of time for the energytransmitter 104 to generate energy at each and every wavelength to begenerated is a generation period. Subsequent to completion of thegeneration period, the energy transmitter 104 may start a new generationperiod thereby allowing multiple measurements. A generation period maybe predetermined. For example, in a predetermined generation period,energy at all desired wavelengths may be generated by the energytransmitter 104.

In some embodiments, for each wavelength, the corresponding energy maybe generated for a time period equal to a predetermined time durationdivided by the number of wavelengths. For example, four (4) wavelengthsmay be determined and the predetermined time duration (e.g., generationperiod) is two (2) seconds. Accordingly, energy for each wavelength maybe generated for a duration of half (0.5) second.

The energy receiver 106 may detect energy 114 associated with energy 112provided by the energy transmitter 104. For example, the energy receiver106 may detect at least a portion of the energy 112 emitted by theenergy transmitter 104 through or reflected from within tissues (e.g.,skin) of the user wearing the photoplethysmography device 100,regardless of whether the photoplethysmography device 100 is properlypositioned. In various embodiments, the energy receiver 106 may detectenergy 114 from the body of the user that is a fraction of the energy112 produced by the energy transmitter 104. The energy receiver 106 maydetect energy 112, at least a portion of which is energy 112 emitted bythe energy transmitter that does not pass through or is otherwisedirected at tissue of the user of the photoplethysmography device 100.For example, the energy receiver 106 may detect energy 114, at least aportion of which is directly transmitted from the energy transmitter aspart of the emitted energy 112 without passing through tissue of a user,when the user is not wearing the photoplethysmography device 100.

The energy transmitter 104 and the energy receiver 106 may be configuredsuch that the energy receiver 106 detects reflected energy from tissuesof the user of the photoplethysmography device 100. For example, theenergy transmitter 104 and the energy receiver 106 may be configured tobe disposed on a surface or side of a user's tissues. The energytransmitter 104 and the energy receiver 106 may be configured such thatthe energy receiver 106 detects energy 114 based on reflected energy 112initially emitted from the energy transmitter 104 that passed through orreflected from the user's tissues. In some embodiments, the energytransmitter 104 and the energy receiver 106 may be configured to bedisposed on different (e.g., opposite) surfaces or sides of a user'stissues.

Energy 114 detected from tissues of a user may be detected by the energyreceiver 106. The energy receiver 106 may be configured to generate asignal in response to detected energy 114. In some embodiments, theenergy receiver 106 may generate different signals or signals withdifferent information depending on the energy (e.g., wavelength)received. The energy receiver 106 may generate an output (e.g.,signal(s)) that depends or partially depends upon the amount of energy114 received. The energy receiver 106 may be configured to generate asignal (e.g., an electric current, or an electric voltage) in responseto the energy 114 received from or through the user's tissues.

The signal(s) generated by the energy receiver 106 may be associatedwith one or more medical sign metrics. For example, a signal generatedby the energy receiver 106 may be associated with one or more bloodmetrics and/or nutrients of interest. Energy at different wavelengthsmay be absorbed at a different rate (e.g., the rate may be related to auser's body state). The user's body state (e.g., heart rate, bloodpressure, nutrient level, or the like) may determine the amount ofenergy absorbed by the body. Accordingly, energy reflected from orpassed through the user's body at different wavelengths may be detectedat different levels thereby causing different responses of the energyreceiver 106. The energy receiver 106 may, for example, output signalsbased on the level of the energy 114 received.

The energy receiver 106 may provide information (e.g., within thesignal) associated with the user's body state. For example, blood metricinformation may be determined (e.g., by an analyzer) from the outputsignal of the energy receiver 106.

The energy receiver 106 may comprise a set of photodetectors (e.g., aphoto diode, or a photo transistor) which may be configured to outputone or more signal(s) dependent upon photons or the like of receivedenergy 114 based at least in part on energy emitted from the energytransmitter 104 that passed through tissues of the user. In variousembodiments, the output signal of the energy receiver 106 is a compositeof multiple signals. Each signal of the composite may be associated withenergy at a wavelength which may be a portion (or fraction) of the totalenergy 112 emitted by the energy transmitter 104.

In various embodiments, the signal validity classification system 108may determine a validity of one or more signal(s) generated by theenergy receiver 106 in response to the received energy 114. Validity ofa signal may indicate whether or not the signal was obtained when thephotoplethysmography device 100 is correctly positioned to measuremedical signs of a user. Whether the photoplethysmography device 100 iscorrectly positioned to measure medical signs of a user may includewhether the user is actually wearing the photoplethysmography device 100when the photoplethysmography device 100 is in operation (e.g., when thephotoplethysmography device 100 is emitting and detecting energy).Additionally, whether the photoplethysmography device 100 is correctlypositioned may include whether the user wearing the photoplethysmographydevice 100 has properly positioned the photoplethysmography device 100to accurately measure medical signs. If it is determined that thephotoplethysmography device 100 is incorrectly positioned (e.g. the useris not wearing the device at all or the user is wearing the device butit is not correctly positioned) then it may be determined that signalsgenerated at the photoplethysmography device 100 are invalid.

The signal validity classification system 108 may be implemented at thephotoplethysmography device 100 itself and may allow for thephotoplethysmography device 100 to self-determine (e.g. without the useof external systems) whether signals generated by the energy receiver106 based on the received energy 114 are valid.

In various embodiments, the signal validity classification system 108determines a validity of a signal by classifying the signal using avalidity classifier to calculate a validity score for the signal. Thevalidity classifier may be applied to one or a plurality of wave metriccalculations of a set of wave metric calculations derived from thesignal. Wave metric calculations include applicable measurements,descriptors, and detectors which may be calculated from a signal orotherwise features of a signal (e.g., one or more waves of the signal).Example wave metric calculations include, but is not limited to, iSQI,bSQI, kSQI (Li), Hjorth Parameters (Deshmane, Oh), Interquartile Ratioor Perfusion, Energy, Zero-crossing Rate (Monte), template fit measures(Gaussian, Dynamic Time Warp, Longest Common Subsequence) (Li,Gartheeban), statistics of Kaiser Teager Energy (Mean, Variance,interquartile ratio, Skew) (Monte), Spectrum Template Matches, SpectralEntropy (Monte), characteristics determined according toDerivative-of-Gaussian filtering methods, periodicity measures, channelsimilarity measures, and characteristics determined accordingWavelet-based detection methods.

In applying one or a plurality of wave metric calculations (e.g. fivemetric calculations), complexity and computation costs of determiningsignal validity is decreased. This may be beneficial as the signalvalidity classification system 108 is implemented at thephotoplethysmography device 100 which, in some embodiments, when thephotoplethysmography device 100 is a wearable device, processing powerand memory is limited.

For example, in some embodiments, the wearable photoplethysmographydevice 100 may be able to determine if the signal is valid by using oneor more tests. If the wearable photoplethysmography device 100determines that the signal is invalid, the photoplethysmography device100 may terminate any additional light emissions, detections,measurements, analysis, and/or the like. If the photoplethysmographydevice 100 determines that the signal is valid, the photoplethysmographydevice 100 may continue to perform additional actions (e.g., emit,detect, measure, analyze, and/or the like). As a result, computationalresources and power may be saved by performing a limited number of testsregarding signal validity rather than performing measurements andanalysis for assessment of the received signal(s).

In various embodiments, the validity classifier, used by the signalvalidity classification system 108, is generated based on wave metriccalculations of a set of wave metric calculations made from previoussignals generated at a plurality of photoplethysmography devices 100.For example, machine learning algorithms may be applied to known validsignals and known invalid signals to build the validity classifier basedon wave metric calculations of the set of wave metric calculations ofboth known valid signals and known invalid signals. The validityclassifier may include values, range of values, or functions of wavemetric calculations of the set of wave metric calculations. For example,the validity classifier may include a range of values of a wave metriccalculation indicating a signal is valid. The creation of the validityclassifier is discussed herein.

In various embodiments, the validity classifier may be specific tocharacteristics of a user of the photoplethysmography device 100. Forexample, the validity classifier may be created based on wave metriccalculations determined from signals received from photoplethysmographydevices worn by users with one or more shared characteristics of theuser (e.g., weight, height, health conditions, or the like). In variousembodiments, the validity classifier used by the signal validityclassification system 108 may be specific to a photoplethysmographydevice type. For example, the validity classifier may be created basedon wave metric calculations determined form signals received from thephotoplethysmography devices of the specific device type.

In various embodiments, the signal validity classification system 108may generate a validity score for a signal based on application of thevalidity classifier to one or a plurality of wave metric calculations ofthe set of wave metric calculations of the signal. For example, thesignal validity classification system 108 may apply the validityclassifier to determined wave metric calculations of signal oscillatoryenergy from a single generated by the energy receiver 106 in response tothe received energy 114 for purposes of determining whether the signalis valid. In another example, the signal validity classification system108 may apply the validity classifier to determine a wave metriccalculation of a comparison of oscillatory energy with its derivativefrom a signal generated by the energy receiver 106 for purposes ofdetermining whether the signal is valid.

In various embodiments, the signal validity classification system 108may compare the validity score to a validity threshold to determine ifone or more signals are valid, and subsequently whether the user iscorrectly wearing the photoplethysmography device 100. For example, thesignal validity classification system 108 may compare the validity scoreto a validity threshold to determine whether the user is wearing thephotoplethysmography device 100 at all or whether the user is wearingthe photoplethysmography device 100 and the device is correctlypositioned. The validity threshold may be a validity score or a range ofvalidity scores. In various embodiments, the signal validityclassification system 108 may determine the signal is valid,corresponding to correct wearing of the photoplethysmography device 100by the user, if the validity score falls at or above the validitythreshold. Alternately, in various embodiments, the signal validityclassification system 108 may determine the signal is valid,corresponding to correct wearing of the photoplethysmography device 100by the user, if the validity score falls at or below the validitythreshold.

The validity threshold is pre-set or selected. For example, the validitythreshold may be pre-set or selected to achieve a specific number oftrue positive results for determining signal validity. In variousembodiments, the validity threshold is pre-set or selected based on oneor a combination of a device type of the photoplethysmography device100, medical signs being measured by the photoplethysmography device100, characteristics of a user of the photoplethysmography device 100,and/or a desired rate of achieving true positive results. For example,if the photoplethysmography device 100 is used to measure respirationrates in an overweight male, then the validity threshold of the validityclassifier may be set to achieve a specific number of true positiveresults in measuring respiration rates in overweight males.

In some embodiments, the user may identify health concerns or healthstate information (e.g., weight), and/or demographic informationregarding the user. The user may identify the information using thephotoplethysmography device 100, an application (e.g., an app on asmartphone or personal computer) in communication with thephotoplethysmography device 100, or a website. The photoplethysmographydevice 100, application, or website may then provide one or moreidentifiers that identify one or more validity thresholds (e.g., asubset of validity thresholds from a set of validity thresholds)associated with the provided information to the photoplethysmographydevice 100 which may then utilize the identified one or more validitythresholds. In some embodiments, the application or website may providethe subset of validity thresholds associated with the informationprovided by the user.

In various embodiments, the signal validity-based device control module110 may control operation of the photoplethysmography device 100 basedon a determination made by the signal validity classification system108. The signal validity-based device control module 110 may controloperation of any of the energy transmitter 104, the energy receiver 106,the signal validity classification system 108, the communication module,the driver, the power source, and/or the analyzer based on thedetermination of whether the photoplethysmography device 100 iscorrectly positioned. For example, if it is determined the signal or aplurality of signals in succession are invalid, indicating thephotoplethysmography device 100 is incorrectly positioned, then thesignal validity-based device control module 110 may instruct, orotherwise cause, any of the energy transmitter 104, the energy receiver106, the signal validity classification system 108, the communicationmodule, the driver, the power source, and/or the analyzer to stopoperation before further analysis is performed, operate in a reducedcapacity, deactivate one or more components of the photoplethysmographydevice 100, or operate to consume a reduced amount of power.

In various embodiments, the signal validity-based device control module110 may control operation of the photoplethysmography device 100 basedon a determination of whether the photoplethysmography device 100 isproperly positioned using the communication module. For example, thesignal validity-based device control module 110 may instruct thecommunication module to send status updates to a remote system,indicating whether the user is correctly wearing thephotoplethysmography device 100 based on a determination of whether thephotoplethysmography device 100 is correctly positioned.

The signal validity-based device control module 110 may receiveinstructions regarding controlling operation of the photoplethysmographydevice 100 from a remote system based on a determination made by thesignal validity classification system 108 regarding validity of one or aplurality of signals generated by the energy receiver 106. For example,the signal validity-based device control module 110 may receiveinstructions, through the communication module from the remote system,indicating an instruction to deactivate the energy transmitter 104, theenergy receiver 106, or any other components of the photoplethysmographydevice 100 if it is determined that signals generated by the energyreceiver 106 are invalid. In various embodiments, the signalvalidity-based device control module 110 may control operation of thephotoplethysmography device 100 based on received instructions made inresponse to a determination of whether the photoplethysmography device100 is correctly positioned.

It will be understood that for some embodiments, the systems and modulesor the arrangement of systems and modules may differ from what isdepicted in FIG. 1.

Each of the systems, modules, analyzers, and various other components ofthe photoplethysmography device 100 may be implemented using one or moredigital devices. An example digital device is described regarding FIG.5. It will be appreciated that one or more of the modules may behardware, software, or a combination of both. In some embodiments, oneor more of the modules may be code within memory (e.g., a nontransitivememory such as a hard drive, SSD, flash drive, or the like). In variousembodiments, one or more of the modules may include a digital signalprocessor (DSP) or application specific integrated chips (ASICs).

FIG. 2 illustrates an example flow diagram of a method 200 ofcontrolling a photoplethysmography device 100 based on a determinationof whether the photoplethysmography device 100 is correctly positionedto measure medical signs of a user. At step 202, a signal is generatedby the energy receiver 106 of the photoplethysmography device 100. Thereceived signal may be generated based on energy received at the energyreceiver 106. Energy received by the photoplethysmography device 100 maybe initially generated by energy transmitter(s) 104 of thephotoplethysmography device 100. The signal generated by thephotoplethysmography device 100 may be generated (e.g., by photodetectors) based on at least a portion of the energy received by thephotoplethysmography device 100.

In various embodiments, the signal generated at step 202, may begenerated based on a portion of energy emitted by the energy transmitter104 through tissue of a user and received at the energy receiver 106.For example, energy may be received at the energy receiver 106 atvarying levels (e.g., intensity levels) based on an amount of energyabsorbed by tissue of a user, and the received signal may be generatedbased on the varying energy levels of the energy received at the energyreceiver 106. Alternately, the signal generated at step 202, may begenerated based on energy received at the energy receiver 106 when thephotoplethysmography device 100 is incorrectly positioned or not beingworn. In some embodiments, wave features of the signal are extracted andwave metrics taken based on the wave features.

At step 204, the signal (e.g., wave features extracted from the signalsand/or wave metrics) is classified using a validity classifier tocalculate a validity score for the signal. In various embodiments, thesignal may be classified using a validity classifier by the signalvalidity classification system 108. In various embodiments, the signalvalidity classification system 108 may be implemented at thephotoplethysmography device 100, and may be used to determine, at thephotoplethysmography device 100, whether the signal received at step 202is valid. As discussed herein, the signal validity classification system108 may determine at the photoplethysmography device 100 whether thephotoplethysmography device 100 is correctly positioned (e.g., beingworn at all or being worn correctly) to measure medical signs of a user.

At step 206, the validity score for the signal is compared to a validitythreshold. The validity score for the signal may be compared to avalidity threshold to determine if the photoplethysmography device 100is correctly positioned or being worn. The signal validityclassification system 108 may compare the validity score of the signalto the validity threshold to determine if the user is wearing thephotoplethysmography device 100, is wearing the photoplethysmographydevice 100 but has the device incorrectly positioned, or is wearing thephotoplethysmography device 100 in a correct position.

As discussed herein, the validity threshold may be pre-set to achieve aspecific number of true positive results in determining whether signalgenerated by the energy receiver 106 of the photoplethysmography device100 are actually valid. Further, in various embodiments, the validitythreshold may be pre-set based on one or a combination of a device typeof the photoplethysmography device 100, medical signs being measured bythe photoplethysmography device 100, characteristics of a user of thephotoplethysmography device 100, and/or a desired rate of achieving truepositive results.

At step 208, operation of the photoplethysmography device 100 iscontrolled based on the determination of whether thephotoplethysmography device 100 is correctly positioned (e.g., based ona comparison of the validity score to the validity threshold). Forexample, the signal validity-based device control module 110 controlsoperation of the photoplethysmography device 100 based on thedetermination of whether the photoplethysmography device 100 iscorrectly positioned.

In various embodiments, the photoplethysmography device 100 may controlitself in operation (e.g. independently from a remote system) based onthe determination of whether the photoplethysmography device 100 iscorrectly positioned. For example, the photoplethysmography device 100may cause itself to shutoff, deactivate, or power down (e.g., sleep orhibernate) in response to a determination that the photoplethysmographydevice 100 is not correctly positioned.

Alternately, the photoplethysmography device 100 may be controlled by aremote system based on the determination of whether thephotoplethysmography device 100 is correctly positioned. For example,the photoplethysmography device 100 may receive instructions regardingpowering itself down from a remote system in response to a determinationthat it is incorrectly positioned. In this example, thephotoplethysmography device 100 may subsequently power itself downaccording to the instructions.

In various embodiments, any of the energy transmitter 104, the energyreceiver 106, the signal validity classification system 108, thecommunication module, the driver, the power source, and the analyzer maybe controlled based on the determination of whether thephotoplethysmography device 100 is correctly positioned. For example,the energy transmitter 104 and the energy receiver 106 may be poweredoff (or power reduced) in response to a determination that thephotoplethysmography device 100 is incorrectly positioned.

FIG. 3 is a system diagram illustrating an example environment 300utilizing a photoplethysmography device 302 in accordance with variousembodiments. As shown in FIG. 3, the example environment 300 comprises awearable photoplethysmography device 302, one or more user systems 304,an optional analysis system 308, and a computer network 306communicatively coupling together each of the multispectralphotoplethysmography device 302, one or more user devices 310, 312, and314 (depicted as user system 304), and/or the analysis system 308. Asshown, a user system 304 may include a smartphone 310 (e.g., iPhone®), acomputer 312 (e.g., a personal computer), and/or a tablet 314 (e.g.,iPad®), through the computer network 306 (e.g., a Bluetooth® 4.0personal area network), may either interact directly or indirectly withthe photoplethysmography device 302. The wearable photoplethysmographydevice 302 may be or include the wearable photoplethysmography device100.

The photoplethysmography device 302 may measure medical signsnon-invasively. For example, the photoplethysmography device 302 may bea multispectral blood metrics measurement apparatus configured tomeasure blood metrics such as concentrations of various nutrients overtime, deliver energy into tissues of various body parts of a user, tracka user's behavior pattern, detect motion, communicate various bloodmetric measurements, and/or receive a user's instructions. In variousembodiments, through the computer network 306, the photoplethysmographydevice 302 may transmit one or more medical sign estimates or signalsrepresenting medical sign metrics to, or receive instructions from, theuser system 304 or the analysis system 308, such as which medical signmetrics to measure.

In some embodiments, the photoplethysmography device 302 may projectenergy into tissues of a user and may receive energy through the tissuesof the user. The photoplethysmography device 302 may project energy intoa tissue of a user when the user is correctly wearing thephotoplethysmography device 302 or, potentially, incorrectly wearing thephotoplethysmography device 302. Additionally, the photoplethysmographydevice 302 may project energy regardless of whether the user is wearingthe photoplethysmography device 302 at all.

In some embodiments, the photoplethysmography device 302 may projectenergy into tissue of a user and detect energy reflected from and/ortransmitted through tissue of the user (e.g., the wearer ofphotoplethysmography device 302). The projected energy may be atmultiple wavelengths that are associated with medical sign estimates(e.g. blood metrics of interest to a user). The detected energy may be afraction of the energy that is projected into the tissue. As discussedherein, energy at different wavelengths may be absorbed at differentrates, each of which may be related to a user's body state. For example,the user's body state (e.g., heart rate, blood pressure, nutrient level,or the like) may determine the amount of absorbed energy. Accordingly,energy at different wavelengths may be absorbed at different levels by auser's body. The fraction of energy received (e.g., that is reflected bythe tissue or transmitted through the tissue) may be used to generatesignals (e.g., composite signals) at different levels. These signals mayprovide information of the user's body state related to medical signs.This information may be obtained by analyzing waveforms of the signal inthe time domain and/or the frequency domain.

The photoplethysmography device 302 may measure any number of medicalsign metrics, including, but not limited to, skin conductivity, pulse,oxygen blood levels, blood pressure, blood glucose level, glycemicindex, insulin index, Vvo2max, fat body composition, protein bodycomposition, blood nutrient level (e.g., iron), body temperature, bloodsodium levels, naturally-produced chemical compound level (e.g., lacticacid), respiration rates, fluid volume, and depth of anesthesia.Nutrients may be determined based on the blood metrics to be measured.Measured nutrients may include, but are not limited to, glucose,hemoglobin, triglycerides, cholesterol, bilirubin, protein, albumin(i.e., egg white), and/or electrolytes (e.g., sodium, potassium,chloride, bicarbonate, or the like).

It will be appreciated that the user's body state may change dynamicallyand energy at a wavelength may be absorbed differently by a user overthe time. By monitoring and tracking detected energy from the user'sbody, a user's health or condition may be tracked. Systems and methodsdescribed herein may monitor and store blood metrics includingconcentrations of various nutrients. A user's history health records maybe generated, logged, and/or stored by medical signs measured atdifferent times the wearable photoplethysmography device 302. In someembodiments, blood metrics measured by the photoplethysmography device302 at a given time point may be compared to the history health recordsto detect any abnormal health conditions. The photoplethysmographydevice 302 may comprise a user interface where a user may input bloodmetrics of interest, be presented with various health reports, and/or bealerted with abnormal health conditions.

In some embodiments, a user may comfortably wear a photoplethysmographydevice 302 over time. The photoplethysmography device 302 may compriselightweight components, made of hypoallergenic materials, and/or includeflexible components so that it could fit various body parts (e.g.,wrist, earlobe, ankle, or chest) of a user.

In accordance with some embodiments, the computer network 306 may beimplemented or facilitated using one or more local or wide-areacommunications networks, such as the Internet, WiFi networks, WiMaxnetworks, private networks, public networks, personal area networks(“PAN”), and the like. In some embodiments, the computer network 106 maybe a wired network, such as a twisted pair wire system, a coaxial cablesystem, a fiber optic cable system, an Ethernet cable system, a wiredPAN constructed with USB and/or FireWire connections, or other similarcommunication network. The computer network 306 may be a wirelessnetwork, such as a wireless personal area network, a wireless local areanetwork, a cellular network, or other similar communication network.Depending on the embodiment, some or all of the communicationconnections associated with the computer network 306 may utilizeencryption (e.g., Secure Sockets Layer [SSL]) to secure informationbeing transferred between the various entities shown in the exampleenvironment 300.

Although FIG. 3 depicts a computer network 306 supporting communicationbetween different digital devices, it will be appreciated that thephotoplethysmography device 302 may be directly coupled (e.g., over acable) with any combination of the user devices 310, 312, and 314.

The user devices 310-314 may include any digital device capable ofexecuting an application that measures, assists in measuring, or postingresults related to blood metrics. An application user interface may bepresented by the application and/or communicating with various entitiesin the example environment 300 through the computer network 306. Forinstance, the user device 310 may receive one or more medical signmeasurements from the photoplethysmography device 302 (e.g., via thecomputer network 306), track and store the medical sign measurements,analyze the medical sign measurements, and/or provide recommendationsbased on the medical sign measurements. An application user interfacemay facilitate interaction between a user of the user system 304 and anapplication running on the user system 304.

In various embodiments, any of the user devices 310-314 may performanalysis of the medical sign measurements using data received from thephotoplethysmography device 302, display results, provide reports,display progress, display historic readings, track measurements, trackanalysis, provide alerts, and/or the like.

The analysis system 308 may be or include any digital device capable ofexecuting an analysis application for analyzing and/or measuring medicalsign metrics. In some embodiments, the analysis system 308 may generatereports or generate alerts based on analysis or measurement of medicalsign metrics. For instance, through the computer network 306, theanalysis system 308 may receive one or more blood metric measurementsfrom the photoplethysmography device 302, track and store blood metricmeasurements, analyze blood metric measurements, and/or providerecommendations based on the analysis. An application programminginterface may facilitate interaction between a user, the user devices310-314, and/or the multispectral blood metrics measurement apparatus310 with the analysis system 308.

In some embodiments, the photoplethysmography device 302, user devices310-314, and/or analysis system 308 may comprise a reference table formeasuring medical sign metrics. For example, the photoplethysmographydevice 302, user devices 310-314, and/or analysis system 308 maycomprise a reference table of blood components, molecules, and/ornutrients and wavelengths corresponding to the blood components,molecules, and/or nutrients. A wavelength may be unique to or moregenerally associated with a nutrient. A reference wavelength may beunique to or more generally associated with a combination of nutrientsto be measured. As such, wavelength(s) may be determined by looking upeach blood components, molecules, and/or nutrients that is to bemeasured. Energy at the determined wavelengths may be transmitted by anenergy transmitter of the photoplethysmography device 302 into the body.

Computing devices (e.g., digital devices) may include a mobile phone, atablet computing device, a laptop, a desktop computer, personal digitalassistant, a portable gaming unit, a wired gaming unit, a thin client, aset-top box, a portable multi-media player, or any other type of networkaccessible user device known to those of skill in the art. Further, theanalysis system 308 may comprise of one or more servers, which may beoperating on or implemented using one or more cloud-based services(e.g., System-as-a-Service [SaaS], Platform-as-a-Service [PaaS], orInfrastructure-as-a-Service [IaaS]).

It will be understood that for some embodiments, the components or thearrangement of components may differ from what is depicted in FIG. 3.

Each of the photoplethysmography device 302, one or more user devices310, 312, and 314, and the analysis system 308 may be implemented usingone or more digital devices. An example digital device is describedregarding FIG. 5.

FIG. 4 illustrates an example photoplethysmography device 400 formeasuring various medical signs in accordance with embodiments of thepresent application. The photoplethysmography device 400 comprises acentral unit 402, a sensor array 404, and a coupling means 408. Thecentral unit 402 may be a wearable member made of elastic and/orflexible hypoallergenic wearable material. The photoplethysmographydevice 400 may be or include the photoplethysmography device 302.

In the illustrated example, the sensor array 404 is coupled to thecentral unit 402. The sensor array 404 may comprise any number of energytransmitters and/or energy receivers. In some embodiments, the sensorarray 404 may be detached from the central unit 402. The sensor array404 may be mechanically and/or electrically coupled to the central unit402. The sensor array 404 comprises various illumination (e.g., nearinfra-red, infra-red, or short infra-red) and sensing arrays. Anillumination array may include any number of energy transmitters (e.g.,any number of energy transmitter 104). A sensing array may include anynumber of energy receivers (e.g., any number of energy receivers 106).The sensor array 404 may further comprise conductivity and/or capacitysensors. Different sensor array 404 may be provided to measure differentmedical signs.

The central unit 402 may comprise an analyzer, one or more energytransmitter(s), and/or one or more energy receiver(s). The central unit402 may further comprise a communication module and/or a batterycompartment. The coupling means 408 may be mounting screw holes in FIG.4, however, it will be appreciated that coupling means may be optional.The coupling means 408 may include or be any kind of coupling meansincluding a clip, hook, switch, expanding fabric, adhesive, or the like.It will be appreciated that other mounting means may be used.

The photoplethysmography device 400 further comprises a micro-USB port406 to allow for communication with a digital device and a screen 410.Various user interfaces (e.g., lights, a display, touchscreen, or thelike) may be displayed on the screen 410.

FIG. 5 is a block diagram of an example digital device 500. The digitaldevice 500 comprises a processor 502, a memory system 504, a storagesystem 506, a communication network interface 508, an I/O interface 510,and a display interface 512 communicatively coupled to a bus 514. Theprocessor 502 is configured to execute executable instructions (e.g.,programs). In some embodiments, the processor 502 comprises circuitry orany processor capable of processing the executable instructions.

The memory system 504 is any memory configured to store data. Someexamples of the memory system 504 are storage devices, such as RAM orROM. The memory system 504 may comprise the RAM cache. In variousembodiments, data is stored within the memory system 504. The datawithin the memory system 504 may be cleared or ultimately transferred tothe storage system 506.

The storage system 506 is any storage configured to retrieve and storedata. Some examples of the storage system 506 are flash drives, harddrives, optical drives, and/or magnetic tape. In some embodiments, thedigital device 500 includes a memory system 504 in the form of RAM and astorage system 506 in the form of flash data. Both the memory system 504and the storage system 506 comprise computer readable media which maystore instructions or programs that are executable by a computerprocessor including the processor 502.

The communications network interface (com. network interface) 508 may becoupled to a network (e.g., the computer network 304) via the link 516.The communication network interface 508 may support communication overan Ethernet connection, a serial connection, a parallel connection, oran ATA connection, for example. The communication network interface 508may also support wireless communication (e.g., 802.11 a/b/g/n, WiMax).It will be apparent to those skilled in the art that the communicationnetwork interface 408 may support many wired and wireless standards.

The optional input/output (I/O) interface 510 is any device thatreceives input from the user and output data. The optional displayinterface 512 is any device that is configured to output graphics anddata to a display. In one example, the display interface 512 is agraphics adapter.

It will be appreciated that the hardware elements of the digital device500 are not limited to those depicted in FIG. 5. A digital device 500may comprise more or less hardware elements than those depicted.Further, hardware elements may share functionality and still be withinvarious embodiments described herein. In one example, encoding and/ordecoding may be performed by the processor 502 and/or a co-processorlocated on a GPU (i.e., Nvidia®).

FIG. 6 is a block diagram of an example signal validity classificationsystem 600. The signal validity classification system 600 determinesvalidity of a signals and/or energy received by an energy receiver 106at the photoplethysmography device 100 (see FIG. 1). The signal validityclassification system 600 may determine validity of a received signalbased on a portion of energy transmitted from the energy transmitter tothe energy receiver of the photoplethysmography device 100 when thephotoplethysmography device 100 is operating to measure medical signswhile not being worn or otherwise is incorrectly positioned.

In various embodiments, the signal validity classification system 600may determine validity of the signals and/or energy based on opticalbeam(s) or other energy transmitted from an energy transmitter at aphotoplethysmography device 100. For example, the signal validityclassification system 600 may determine validity of a received signalbased on a portion of energy directed to tissue of a user by the energytransmitter of a photoplethysmography device 100 after passing through,reflecting off of, or otherwise interacting with at least some tissue ofa user.

The signal validity classification system 600 may determine validity ofa received signal when the photoplethysmography device 100 is not worn.For example, the signal validity classification system 600 may determinevalidity of a received signal based on a portion of energy transmittedfrom the energy transmitter to the energy receiver of thephotoplethysmography device 100 without that energy passing through,reflecting off of, or otherwise interacting with at least some tissue ofa user.

In various embodiments, the signal validity classification system 600may determine whether the photoplethysmography device 100 is correctlypositioned to measure medical signs of a user. For example, the signalvalidity classification system 600 may determine thephotoplethysmography device 100 is not being worn at all. In anotherexample, as part of determining that the photoplethysmography device 100is incorrectly positioned, the signal validity classification system 600may determine the photoplethysmography device 100 is being worn but hasincorrect placement to enable functionality.

In various embodiments, the signal validity classification system 600 isimplemented at a photoplethysmography device 100. The signal validityclassification system 600 may determine whether signals generated at thephotoplethysmography device 100 are valid.

Alternately, all or portions of the signal validity classificationsystem 600 may be implemented remote from the photoplethysmographydevice 100. In being implemented remotely from the photoplethysmographydevice 100, the signal validity classification system 600 may remotelydetermine whether signals generated at the photoplethysmography device100 are valid. For example, the signal validity classification system600 may, at a remote location, receive a signal generated at thephotoplethysmography device 100 (e.g., by the energy receiver inresponse to detecting energy initially transmitted by the energytransmitter), determine whether the signal(s)/energy received by thephotoplethysmography device 100 are valid, and potentially allow forremote control of operation (e.g., instructions to activate parts of thephotoplethysmography device 100, deactivate parts of thephotoplethysmography device 100, continue to perform additional testsand analysis to estimate medical conditions/signs of the user, perform asubset of tests and analysis to estimate medical conditions/signs of theuser, test validity of received signal(s) again, and/or the like).

In various embodiments, a determination of whether signals received at aphotoplethysmography device 100 are valid, as made by the signalvalidity classification system 600, may be used to control operation ofthe photoplethysmography device. For example, one or a combination of anenergy transmitter 104, an energy receiver 106, a signal validityclassification system 108, a communication module, a driver, a powersource, an analyzer, and other applicable modules or components of aphotoplethysmography device may be controlled based on a determinationof whether signals generated at the photoplethysmography device arevalid.

The signal validity-based device control module 110 may controloperation of the photoplethysmography device 100 based on adetermination of whether signals received at the photoplethysmographydevice 100 are valid. For example, the signal validity-based devicecontrol module 110 may control operation of one or a combination of anenergy transmitter 104, an energy receiver 106, a signal validityclassification module 110, a communication module, a driver, a powersource, an analyzer, and other applicable modules or components of aphotoplethysmography device 100 may be controlled based on adetermination, as made by the signal validity classification system 600,of whether signals generated at the photoplethysmography device 100 arevalid.

The example signal validity classification system 600 shown in FIG. 6includes a signal input module 602, a wave feature selection module 604,a wave metric calculation determination module 606, a validityclassifier datastore 608, a wave metric-based signal validityclassification module 610, a smoothing module 612, and a validityprediction module 614. The signal input module 602 may receive signalsgenerated at a photoplethysmography device 100 based on energy receivedby an energy receiver 106 of the photoplethysmography device 100 (e.g.,signals generated by a photo detector that detected light energyreflected from the tissue of the user).

In various embodiments, the signal input module 602 receives signalsgenerated at a photoplethysmography device 100 based on at least aportion of energy emitted from an energy transmitter 104 of thephotoplethysmography device 100 and received at an energy receiver 106of the photoplethysmography device 100. For example, the signal inputmodule 602 may receive a signal generated based on a portion of energyinitially generated by the energy transmitter 104 of thephotoplethysmography device 100 and received at the energy receiver 106after passing through, reflecting off of, or otherwise interacting withthe tissue. In another example, the signal input module 602 may receivea signal generated based on a portion of energy transmitted from theenergy transmitter 104 to the energy receiver 106 of thephotoplethysmography device 100 without passing through, reflecting offof, or otherwise interacting with tissue of the user.

The wave feature selection module 604 selects (or “extracts”) a subsetof wave features from a plurality of wave features of the signalreceived from the signal input module 602. As discussed herein, thesignal(s) as well as wave features of the signal(s) generated by theenergy receiver 106 are related to the detected energy. Wave featuresinclude applicable features associated with a wave of a signal. Forexample, wave features may include waves, wave peaks, wave valleys, waveedges, and/or the like.

The wave feature selection module 604 may select waves from the signalreceived by the signal input module 602 for use in selecting wavefeatures from the signal. The wave feature selection module 604 mayselect “high quality” waves from the signal that match typical wavesgenerated by the photoplethysmography device 100 for measuring aspecific medical sign. For example, the wave feature selection module604 may select waves with generally desired wave features. In someembodiments, the wave feature selection module 604 may select waves toextract wave features based on one or a combination of medical signs thephotoplethysmography device 100 may measure, characteristics of a userof the photoplethysmography device 100, and/or a device type of thephotoplethysmography device 100.

The wave feature selection module 604 may select waves from the signalaccording to wave selection rules. For example, the wave featureselection module 604 may identify the wave valleys whose frequencymatches with the heart rate of the user and then checking how close thewave is to a bi-Gaussian model, according to wave selection rules, aspart of selecting waves according to wave selection rules. The waveselection rules may be provided remotely, by the user (e.g., through anapplication that communicates with the photoplethysmography device 100or an interface on the photoplethysmography device 100), or previouslyconfigured within the photoplethysmography device 100.

In various embodiments, the wave feature selection module 604 maynormalize the signal(s) received by the signal input module 602, as partof selecting a subset of wave features from the plurality of wavefeatures of the signal. For example, the wave feature selection module604 may divide the signal by the mean of the signal and subtract the newsignal mean of one from the signal to normalize the signal. It will beappreciated that any normalization may be used.

The wave metric calculation determination module 606 determines wavemetric calculations based on the selected wave features. In variousembodiments, the wave metric calculation determination module 606determines wave metric calculations of a set of the potential wavemetric calculations described with reference to FIG. 1. For example, aset of wave metric calculations may include one or a combination ofsignal energy measurements, signal mobility measurements, signalcomplexity measurements, signal crossing rate measurements, and signalnon-oscillatory component measurements.

In various embodiments, the wave metric calculation determination module606 may determine wave metric calculations for the signal received bythe signal input module 602 based on the wave features selected by thewave feature selection module 604 from the signal. For example, the wavemetric calculation determination module 606 may determine one or acombination of signal energy measurements, signal mobility measurements,signal complexity measurements, signal crossing rate measurements, andsignal non-oscillatory component measurements based on the wave featuresselected by the wave feature selection module 604. The wave metriccalculation determination module 606 may determine wave metriccalculations for the signal by independently determining correspondingwave metric calculations for each of the subset of wave features of theplurality of wave features selected by the wave feature selection module604. For example, the wave metric calculation determination module 606may separately determine signal mobility measurements and signalcomplexity measurements for the each of the selected subset of wavefeatures of the plurality of wave features of the signal, as part ofdetermining wave metric calculations for the signal.

The validity classifier datastore 608 may store one validity classifieror a plurality of data classifiers. In some embodiments, the validityclassifier datastore 608 stores validity classifier data indicating anynumber of validity classifiers of a plurality of validity classifierscapable of being applied to wave metric calculations, for use indetermining validity of the signal received by the signal input module602. In various embodiments, the validity classifier datastore 608 maystore validity classifier data indicating a validity classifier based onwave metric calculations, potentially including the set of wave metriccalculations determined by the wave metric calculation determinationmodule 606. The validity classifier datastore 608 may store a validityclassifier including a range of values of signal mobility measurementsfor application to signal mobility measurements of the signal, asdetermined by the wave metric calculation determination module 606.

In various embodiments, the validity classifier datastore 608 stores avalidity classifier associated with or specific to any number ofcharacteristics of a user of a photoplethysmography device 100. Forexample a validity classifier stored in the validity classifierdatastore 608 may be specific to males within a certain age rangeexhibiting similar medical signs.

In various embodiments, the validity classifier datastore 608 storesvalidity thresholds for use in comparing to validity scores determinedthrough application of a validity classifier to wave metriccalculations. For example, the validity classifier datastore 608 maystore a validity threshold specifying a specific validity score or rangeof validity scores to compare determined validity scores. The comparisonmay assist in determining whether a user of the photoplethysmographydevice 100 is correctly wearing the device. A validity threshold storedin the validity classifier datastore 608 may be specific to a validityclassifier. For example, a validity threshold specific to a validityclassifier may be applied when validity scores are determined for asignal using the validity classifier.

In various embodiments, the validity classifier datastore 608 stores oneor a plurality of pre-set and selectable validity thresholds. A pre-setand selectable validity threshold stored in the validity classifierdatastore 608 may be specific to one or a combination of a device typeof a photoplethysmography device 100, medical signs being estimated bythe photoplethysmography device 100, characteristics of a user of thephotoplethysmography device 100, and a desired rate of achieving truepositive results. For example, a validity classifier stored in thevalidity classifier datastore 608 may include a validity thresholdspecific to achieving a certain number of true positive results, inapplying a validity classifier in determining validity of signalsreceived by a photoplethysmography device measuring blood pressure ormedical signs in females over the age of fifty.

The pre-set and selectable validity thresholds stored in the validityclassifier datastore 608 may be specifically associated with a validityclassifier. For example, the validity classifier datastore 608 may storepre-set and selectable validity thresholds to use with a specificclassifier in determining validity of signals generated for measuringdifferent medical signs using the specific classifier.

The wave metric-based signal validity classification module 610classifies the signal using a validity classifier. In variousembodiments, the wave metric-based signal validity classification module610 may apply the validity classifier, as indicated by validityclassifier data stored in the validity classifier datastore 608, to thewave metric calculations of the set of wave metric calculations togenerate one or a plurality of validity scores. For example, the wavemetric-based signal validity classification module 610 may input thewave metric calculations into a function, included as part of thevalidity classifier, to determine if the results fall within a range ofvalues of the function, as part of applying the validity classifier.Further in the example, the validity classification module 610 may inputthe wave metric calculations into a trained support vector machineforming part of the classifier. In classifying the signal using thevalidity classifier, the wave metric-based signal validityclassification module 610 may generate one or a plurality of validityscores for the signal, for use in determining whether the receivedsignals are valid (i.e., may be used to estimate medical signs of theuser). For example, in classifying the signal using the validityclassifier, the wave-metric based signal validity classification module610 may apply a validity classifier to corresponding wave metriccalculations generated for each (or one or more) wave feature of the setof wave features, to generate a plurality of validity scorescorresponding to the respective each wave feature of the set of wavefeatures.

In various embodiments, the wave metric-based signal validityclassification module 610 applies a classifier fit to a logisticfunction to the wave metric calculations. For example, the wavemetric-based signal validity classification module 610 may apply aclassifier fit to the logistic function as shown in Example Equation 7herein. In applying a classifier fit to a logistic function to the wavemetric calculations, the wave metric-based signal validityclassification module 610 may calculate a validity score falling withina specific range of values based on a logistic function. For example,the wave metric-based signal validity classification module 610 mayapply a validity classifier fit to a logistic function to the wavemetric calculations to generate a validity score falling between 0 and1.

In various embodiments, the wave metric-based signal validityclassification module 610 selects a specific validity classifier toapply to the wave metric calculations. The wave metric-based signalvalidity classification module 610 may select a specific classifier toapply to the wave metric calculations based on one or a combination of adevice type of a photoplethysmography device 100, medical signs beingmeasured by the photoplethysmography device 100, characteristics of auser of the photoplethysmography device 100, and a desired rate ofachieving true positive results. For example, the wave metric-basedsignal validity classification module may apply a validity classifierspecific to a photoplethysmography device 100 used in determiningrespiration rates of patients.

The smoothing module 612 optionally applies temporal smoothing to theresulting validity scores generated through application of the validityclassifier to the wave metric calculations to generate a smoothedvalidity score. The smoothing module 612 may apply an applicabletemporal smoothing method, e.g. a rank smoothing method, to theresulting validity scores generated through application of the validityclassifier to the wave metric calculations. For example, the smoothingmodule 612 may apply a rank filter causing selection of the thirdhighest validity score of the previous eight determined validity scoresas a smoothed validity score. In various embodiments, as the wavemetric-based signal validity classification module 610 may determineeach validity score independently, application of smoothing may reducenoise causing prediction errors.

The validity prediction module 614 compares one or a plurality of thedetermined validity scores to a validity threshold to determine if thephotoplethysmography device 100 is correctly positioned to measure(e.g., estimate) medical signs of a user. In various embodiments, thevalidity prediction module 614 may determine the photoplethysmographydevice 100 is correctly positioned if one or a combination of theplurality of the determined validity scores are above a validitythreshold. For example, if a majority of the validity scores generatedfor the signal fall above a validity threshold, then the validityprediction module 614 may determine the photoplethysmography device 100is positioned correctly. In various embodiments, the validity predictionmodule 614 may compare a determined smoothed validity score, as filteredusing the smoothing module 612, to a validity threshold to determine ifthe photoplethysmography device 100 is correctly positioned to measuremedical signs of a user. For example, if a determined smoothed validityscore filtered using the smoothing module 612 is above a validitythreshold, then the validity prediction module 614 may determine thephotoplethysmography device 100 is correctly positioned.

In various embodiments, the validity prediction module 614 may select apre-set validity threshold to compare to one or a plurality of validitythresholds for determining validity of the received signal. The validityprediction module 614 may select a pre-set validity threshold based on avalidity classifier chosen by the wave-metric based signal validityclassification module 610. Additionally, the validity prediction module614 may select a pre-set validity threshold based on one or acombination of a device type of a photoplethysmography device 100,medical signs being measured by the photoplethysmography device 100,characteristics of a user of the photoplethysmography device 100, and adesired rate of achieving true positive results. For example, if thephotoplethysmography device 100 is used to measure blood pressure, thena pre-set and selectable validity threshold used in determining validityof signals generated by photoplethysmography devices 100 may be selectedby the validity prediction module 614.

It will be understood that for some embodiments, the modules anddatastores or the arrangement of modules and datastores may differ fromwhat is depicted in FIG. 6.

Each of the modules and datastore of the signal validity classificationsystem 600 may be implemented using one or more digital devices. Anexample digital device is described regarding FIG. 5.

FIG. 7 depicts an example flow diagram of a method 700 of determiningwhether a photoplethysmography device 100 is positioned correctly byapplying a validity classifier to a signal generated by thephotoplethysmography device 100 in operation. In various embodiments,the method 700 is implemented by the signal validity classificationsystem 600. The validity classification system 600 performing the stepsof the method 700 may be implemented at a photoplethysmography device100, for purposes of determining at the photoplethysmography device 100whether the device is correctly positioned to measure medical signs of auser. Additionally, the validity classification system 600 performingthe steps of the method 700 may be implemented remote from aphotoplethysmography device 100, for purposes of determining remotelyfrom the photoplethysmography device 100, whether the device iscorrectly positioned.

At step 702, optical beam(s) and/or signal(s) are received from anenergy receiver of a wearable photoplethysmography device 100. Theoptical beam(s) and/or signal(s) may be initially transmitted from anenergy transmitted through tissue(s) of the user. The energy receivermay receive the optical beam(s) and/or signals through the tissues ofthe user (e.g., the energy receiver may receive that energy that was notabsorbed or reflected away from the energy receiver). The energyreceiver may generate one or more signals based on the optical beam(s)and/or signal(s) received or detected. The signal may be received froman energy receiver of the wearable photoplethysmography device 100 bythe signal input module 602.

In various embodiments, the signal received at step 702 may be receivedeven if the wearable photoplethysmography device 100 is not positionedcorrectly to measure medical signs of a user. The signal received atstep 702 may be generated based on energy received at an energyreceiver, when a user is not wearing a wearable photoplethysmographydevice. In various embodiments, the signal received at step, may begenerated based on energy received at an energy receiver, when a user iswearing a wearable photoplethysmography device 100 but it is notpositioned correctly to measure medical signs of a user.

At step 704, a subset of wave features of a plurality of wave featuresare selected from the signal generated by the energy receiver inresponse to detecting the optical beam(s) and/or signal(s). The wavefeature selection module 604 may select the plurality of wave featuresfrom the signal. In various embodiments, specific waves of the signal toextract wave features from may be selected from the signal. Waves of thesignal to extract wave features from may be selected based on one or acombination of medical signs the wearable photoplethysmography device100 is estimating or measuring, characteristics of a user of thewearable photoplethysmography device 100, and/or a device type of thewearable photoplethysmography device 100. For example, specific waves ofthe signal to extract wave features from may be selected based on if thewearable photoplethysmography device 100 is being used to measure bloodpressure of a user. Further, in various embodiments, specific waves toextract wave features from may be selected from the signal based on waveselection rules. For example, wave valleys whose frequency matches aheart rate of a user may be determined according to wave selection rulesand subsequently waves corresponding to the wave valleys may be selectedfor use in extracting wave features.

In various embodiments, the signal may be normalized before wavefeatures of a plurality of wave features of the signal are selected. Forexample, the signal may be divided by a mean of the signal and the newsignal mean of one may be subtracted from the signal to normalize thesignal.

At step 706, wave metric calculations of a set of wave metriccalculations are determined from the subset of wave features of theplurality of wave features selected from the signal. The wave metriccalculation determination module 606 may determine wave metriccalculations of a set of wave metric calculations from the subset ofwave features of the plurality of wave features selected from thesignal. In various embodiments, wave metric calculations of a set of thepotential wave metric calculations described with reference to FIG. 1are determined from the subset of wave features of the plurality of wavefeatures. For example, a set of wave metric calculations determined fromthe subset of wave features may include one or a combination of signalenergy measurements, signal mobility measurements, signal complexitymeasurements, signal crossing rate measurements, and signalnon-oscillatory component measurements.

In various embodiments, wave metric calculations may be determinedindependently for each of the subset of wave features of the pluralityof wave features selected for the signal. For example, the wave metriccalculation determination module 606 may determine separate signalmobility measurements and signal complexity measurements for each wavefeature of the subset of wave features of the plurality of wave featuresof the signal, as part of determining wave metric calculations for thesubset of wave features.

At step 708, the signal is classified by applying a validity classifierto at least one of the wave metric calculations to generate one or morevalidity scores for the signal. The wave metric-based signal validityclassification module 610 may classify the signal by applying a validityclassifier to at least one of the wave metric calculations to generateone or more validity scores for the signal. A validity classifier may beapplied to at least one of the wave metric calculations using validityclassification data stored in the validity classifier datastore 608.

In various embodiments, a specific validity classifier to apply to atleast one of the wave metric calculations to generate one or morevalidity scores for the signal may be selected. A specific classifier toapply to the at least one of the wave metric calculations may beselected based on one or a combination of a device type of the wearablephotoplethysmography device 100, medical signs being measured by thewearable photoplethysmography device 100, characteristics of a user ofthe wearable photoplethysmography device 100, and a desired rate ofachieving true positive results. For example, the wave metric-basedsignal validity classification module may apply a validity classifierspecific to the wearable photoplethysmography device 100 if the deviceis used in determining respiration rates of patients.

In various embodiments, a specific validity classifier applied to atleast one of the wave metric calculations is fit to a logistic function.For example, the validity classifier may be fit to a logistic to causethe resulting one or more validity scores created through application ofa specific validity classifier to fall between the values of 0 and 1.

At optional step 710, temporal smoothing is applied to one or more ofthe validity scores to generate a smoothed validity scores. Thesmoothing module 602 may apply temporal smoothing to the one or morevalidity scores to generate a smoothed validity score using anapplicable temporal smoothing method. For example, a rank smoothingmethod may be applied to the one or more of the validity scores togenerate a smoothed validity score.

At step 712, the one or more validity scores are compared to a validitythreshold to determine if the wearable photoplethysmography device 100is correctly positioned in operating to measure medical signs of a user.The validity prediction module 614 may compare the one or more validityscores to a validity threshold to determine if the wearablephotoplethysmography device 100 is correctly positioned. For example, ifa majority of validity scores fall above a validity threshold, then itmay be determined that the wearable photoplethysmography device iscorrectly positioned. Conversely, in another example, if a majority ofvalidity scores fall below a validity threshold, then it may bedetermined that the wearable photoplethysmography device 100 isincorrectly positioned in operating to measure medical signs of a user.

In various embodiments, a smoothed validity score may be compared to avalidity threshold to determine if the wearable photoplethysmographydevice 100 is correctly positioned in operating to measure medical signsof a user. For example, if a smoothed validity score falls above avalidity threshold, then it may be determined the wearablephotoplethysmography device 100 is correctly positioned in operating tomeasure medical signs of a user.

In various embodiments, a validity threshold applied at step 712 isselected pre-set validity threshold. A validity threshold applied atstep 712 may be selected based on the validity classifier applied atstep 708. For example, a specific validity threshold associated with thevalidity classifier may be selections. Additionally, a validitythreshold applied at step 712 may be selected based on one or acombination of a device type of the wearable photoplethysmography device100, medical signs being measured by the wearable photoplethysmographydevice 100, characteristics of a user of the wearablephotoplethysmography device 100, and a desired rate of achieving truepositive results. For example, if the wearable photoplethysmographydevice 100 is used to measure blood pressure, then a validity thresholdspecific to determining whether signals generated by wearablephotoplethysmography devices 100 measuring blood pressure are valid maybe selected and applied at step 712.

FIG. 8 is a block diagram of an example wave metric calculationdetermination module 800. The wave metric calculation determinationmodule 800 determines wave metric calculations from a signal receivedfrom the energy receiver to determine if a photoplethysmography device100 that generated the signal is correctly positioned in operating tomeasure medical signs of a user. The wave metric calculationdetermination module 800 may determine wave metric calculations for asubset of wave features of a plurality of wave features selected from asignal generated by a photoplethysmography device. Additionally, thewave metric calculation determination module 800 may independentlydetermine wave metric calculations for each wave feature of a subset ofwave features selected from a signal.

The example signal validity classification system 800 shown in FIG. 8includes a wave feature input module 802, a signal energy determinationmodule 804, a signal mobility determination module 806, a signalcomplexity determination module 808, a signal crossing ratedetermination module 810, a signal non-oscillatory componentdetermination module 812, and a wave metric datastore 814. The wavefeature input module 802 receives wave features selected from a signalgenerated by a photoplethysmography device 100. The wave feature inputmodule 802 may receive wave features selected by the wave featureselection module 604. For example, the wave feature input module 802 mayreceive a subset of wave features selected from a plurality of wavefeatures of a signal.

In various embodiments, the wave feature input module 802 receives wavefeatures selected from a normalized signal. The wave feature inputmodule 802 may receive wave features selected from a signal normalizedaccording to an applicable signal normalization technique. For example,the wave feature input module 802 may receive wave features selectedfrom a signal normalized by dividing the signal with the mean of thesignal and subtracted by a new signal mean of one.

The signal energy determination module 804 determines signal energymeasurements for the wave features received by the wave feature inputmodule 802. A signal energy measurement determined by the signal energydetermination module 804 may be included as part of determined wavemetric calculations for the wave features. The signal energydetermination module 804 may determine a signal energy measurement usingan applicable signal oscillatory energy measurement technique. Examplesignal oscillatory energy measurement techniques include one or acombination of skewness technique, kurtosis techniques, and measurementsof an absolute deviation from a mean. Example Equation 1, shown below,illustrates an example technique of the signal energy determinationmodule 804 determining signal energy measurements for the wave features.E(s[t])=var(s[t])  Example Equation 1

In Example Equation 1, s[t] represents a signal window of the signalincluding one or a plurality of wave features for which wave metriccalculations are determined. E represents a signal energy measurementdetermined by the signal energy determination module 804. Additionally,var represents a signal oscillatory energy measurement technique appliedby the signal energy determination module 804 to determine the signalenergy measurement E as a function of a specific signal window.

The signal mobility determination module 806 determines signal mobilitymeasurements for the wave features received by the wave feature inputmodule 802. A signal mobility measurement determined by the signalmobility determination module 806 may be included as part of determinedwave metric calculations for the wave features. The signal mobilitydetermination module 806 may determine a signal mobility measurementusing an applicable technique for comparing oscillatory energy in thesignal and its derivative. For example, the signal mobilitydetermination module 806 may apply the Hjorth mobility parametertechnique to determine signal mobility. Example Equation 2, shown below,illustrates an example of application of the Hjorth mobility parametertechnique by the signal mobility determination module 806 fordetermining signal mobility measurements for the wave features.

$\begin{matrix}{{M\left( {s\lbrack t\rbrack} \right)} = \sqrt{\frac{E\left( {\frac{ds}{dt}{s\lbrack t\rbrack}} \right)}{E\left( {s\lbrack t\rbrack} \right)}}} & {{Example}\mspace{14mu}{Equation}\mspace{14mu} 2}\end{matrix}$

In Example Equation 2, E represents the signal energy measurementsdetermined by the signal energy determination module 804, for example,according to Example Equation 1. M represents the signal mobilitymeasurement as a function of the specific signal window. The signalmobility measurement as a function of the specific signal window is thesquare root of the signal energy measurement of the derivative of thespecific signal window divided by the signal energy measurement of thespecific signal window.

The signal complexity determination module 808 determines signalcomplexity measurements for the wave features received by the wavefeature input module 802. A signal complexity measurement determined bythe signal complexity determination module 808 may be included as partof determined wave metric calculations for the wave features. The signalcomplexity determination module 808 may determine a signal mobilitymeasurement using an applicable technique for comparing oscillatoryenergy in the signal and second derivatives. For example, the signalcomplexity determination module 808 may apply the Hjorth complexityparameter technique to determine signal complexity. Example Equation 3,shown below, illustrates an example of application of the Hjorthcomplexity parameter technique by the signal complexity determinationmodule 808 for determining signal complexity measurements for the wavefeatures.

$\begin{matrix}{{C\left( {s\lbrack t\rbrack} \right)} = \sqrt{\frac{M\left( {\frac{ds}{dt}{s\lbrack t\rbrack}} \right)}{M\left( {s\lbrack t\rbrack} \right)}}} & {{Example}\mspace{14mu}{Equation}\mspace{14mu} 3}\end{matrix}$

In Example Equation 3, M represents the signal mobility measurementsdetermined by the signal mobility determination module 806.Additionally, C represents the signal complexity measurement as afunction of the specific signal window. The signal complexitymeasurement as a function of the specific signal window is the squareroot of the signal mobility measurement of the derivative of thespecific signal window divided by the signal mobility measurement of thespecific signal window.

The signal crossing rate determination module 810 determines signalcrossing rate measurements for the wave features received by the wavefeature input module 802. A signal crossing rate measurement, determinedby the signal crossing rate determination module 810, may be included aspart of determined wave metric calculations for the wave features. Thesignal crossing rate determination module 810 may determine a signalcrossing rate measurement using an applicable technique for determininga crossing rate, with respect to a reference point, of a signal within asignal window. Example Equation 4, shown below, illustrates an exampleof application of an applicable technique for determining a signalcrossing rate measurement by the signal crossing rate determinationmodule 810 for the wave features.

$\begin{matrix}{{Z\left( {s\lbrack t\rbrack} \right)} = \frac{\sum\limits_{t = 0}^{{T*{Fs}} - 1}\delta_{{- {{sign}{({s{\lbrack t\rbrack}})}}},{{sign}{({s{\lbrack{t + 1}\rbrack}})}}}}{T}} & {{Example}\mspace{14mu}{Equation}\mspace{14mu} 4}\end{matrix}$

The function illustrated in Example Equation 4 may be used by the signalcrossing rate determination module 810 to determine a zero-crossing ratefor the wave features. It is appreciated that the signal crossing ratedetermination module 810 may apply other functions to determiningcrossing rates with respect to other reference points. In ExampleEquation 4, Z represents the zero-crossing rate as a function of thespecific signal window. Additionally, T represents the samplinginterval, while F_(s) represents the sampling frequency.

The signal non-oscillatory component determination module 812 determinessignal non-oscillatory component measurements for the wave featuresreceived by the wave feature input module 802. A signal non-oscillatorycomponent measurement determined by the signal non-oscillatory componentdetermination module 812 may be included as part of determined wavemetric calculations for the wave features. Example, signalnon-oscillatory component measurements include a mean of a signal, asmoothed measure of the magnitude of non-oscillatory components of asignal, a median of a signal, and a mode of a signal. The signalnon-oscillatory component determination module 812 may determine signalnon-oscillatory component measurements using applicable technique fordetermining signal non-oscillatory component measurements.

In various embodiments, any of the signal energy determination module804, the signal mobility determination module 806, the signal complexitydetermination module 808, the signal crossing rate determination module810, and the signal non-oscillatory component determination module 812may determine corresponding measurements included as part of wave metriccalculations using wave features selected from a normalized signal. Forexample, signal energy measurements, signal mobility measurements,signal complexity measurements, signal crossing rate measurements, andsignal non-oscillatory component measurements may be determined for wavefeatures selected from a normalized signal.

The wave metric datastore 814 stores wave metric data indicating wavemetrics determined for wave features by any of the signal energydetermination module 804, the signal mobility determination module 806,the signal complexity determination module 808, the signal crossing ratedetermination module 810, and the signal non-oscillatory componentdetermination module 812. For example, the wave metric datastore 814 maystore wave metric data indicating signal energy measurements, signalmobility measurements, signal complexity measurements, signal crossingrate measurements, and signal non-oscillatory component measurements forselected wave features.

It will be understood that for some embodiments, the modules anddatastores or the arrangement of modules and datastores may differ fromwhat is depicted in FIG. 8.

Each of the modules and datastore of the wave metric calculationdetermination module 800 may be implemented using one or more digitaldevices. An example digital device is described regarding FIG. 5.

FIG. 9 depicts an example flow diagram of a method 900 of determiningwave metrics of a set of wave metrics for a subset of wave features. Atstep 902, the wave feature selection module 604 may collect a subset ofwave features of a plurality of wave features selected from a signalgenerated by an energy receiver of a wearable photoplethysmographydevice 100 generated based upon energy received at the energy receiverare collected. Alternately, a wave feature input module may collect thesubset of wave features of the plurality of wave features selected froma signal generated by an energy receiver of a wearablephotoplethysmography device.

At step 904, the signal energy determination module 804 may determinesignal energy measurements are determined for the subset of wavefeatures. Signal energy measurements determined for the subset of wavefeatures may be included as part of wave metric calculations used todetermine validity of the signal for purposes of determining whether thewearable photoplethysmography device 100 is correctly positioned. Signalenergy measurements may be determined using an applicable technique formeasuring signal energy, such as one or a combination of skewnesstechnique, kurtosis techniques, and measuring of an absolute deviationfrom a mean.

At step 906, the signal mobility determination module 806 determinessignal mobility measurements for the subset of wave features. Signalmobility measurements determined for the subset of wave features may beincluded as part of wave metric calculations used to determine validityof the signal for purposes of determining whether the wearablephotoplethysmography device 100 is correctly positioned. Signal mobilitymeasurements may be determined using an applicable technique formeasuring signal mobility, such as application of a Hjorth mobilityparameter (e.g., see Example Equation 2). Alternately, signal mobilitymeasurements may be determined using signal energy measurementsdetermined at module 904.

At step 908, the signal complexity determination module 808 signalcomplexity measurements are determined for the subset of wave features.Signal complexity measurements determined for the subset of wavefeatures may be included as part of wave metric calculations used todetermine validity of the signal for purposes of determining whether thewearable photoplethysmography device 100 is correctly positioned. Signalcomplexity measurements may be determined using an applicable techniquefor measuring signal complexity, such as application of a Hjorthcomplexity parameter (e.g., see Example Equation 3). Additionally,signal complexity measurements may be determined using signal mobilitymeasurements determined at module 906.

At step 910, the signal crossing rate measurements determination module810 determines signal crossing rate measurements for the subset of wavefeatures. Signal crossing rate measurements determined for the subset ofwave features may be included as part of wave metric calculations usedto determine validity of the signal for purposes of determining whetherthe wearable photoplethysmography device 100 is correctly positioned.Signal crossing rate measurements may be determined using an applicabletechnique for measuring signal crossing rates (e.g., see ExampleEquation 4). Additionally, signal complexity measurements may bedetermined using signal mobility measurements determined at module 906.

At step 912, the signal non-oscillatory component determination module812 signal determines non-oscillatory components for the subset of wavefeatures. Signal non-oscillatory components determined for the subset ofwave features may be included as part of wave metric calculations usedto determine validity of the signal for purposes of determining whetherthe wearable photoplethysmography device 100 is correctly positioned.Signal non-oscillatory components may be determined using an applicabletechnique for determining signal non-oscillatory components, such ascalculating a mean of a signal.

FIG. 10 is a block diagram of an example validity classifier buildersystem 1000. The validity classifier builder system 1000 generates avalidity classifier used in determining validity of a signal forpurposes of determining if a wearable photoplethysmography device iscorrectly positioned. In various embodiments, the validity classifierbuilder system 1000 is implemented remote from wearablephotoplethysmography devices using the classifier for purposes ofdetermining validity of signals received at the wearablephotoplethysmography devices 100. For example, the validity classifierbuilder system 1000 may be remote from wearable photoplethysmographydevices 100 and subsequently provide a generated validity classifier tothe wearable photoplethysmography devices 100.

The validity classifier builder system 1000 includes a validityclassifier builder data gathering module 1002, a wave metric calculationselection module 1004, a validity classifier training module 1006, avalidity classifier tuning module 1008, and a validity classifierdatastore 1010.

The validity classifier builder data gathering module 1002 gathers dataused in building a validity classifier for purposes of determiningwhether signals received by a wearable photoplethysmography device arevalid. The validity classifier builder data gathering module 1002 mayreceive signals generated by one or a plurality of wearablephotoplethysmography device. Signals received by the validity classifierbuilder data gathering module 1002 may include signals generated by oneor a plurality of wearable photoplethysmography devices 100 in operationto measure medical signs. For example, the validity classifier builderdata gathering module 1002 may receive signals generated by energyreceivers of wearable photoplethysmography devices 100 in response toabsorbed energy at the energy receivers. The validity classifier builderdata gathering module 1002 receives signals from an energy receiverand/or an energy transmitter of a wearable photoplethysmography device100 at a sampling rate of 15 Hz. Additionally, the validity classifierbuilder data gathering module 10002 may filter, e.g. using a low-passfilter, received signals.

In various embodiments, the validity classifier builder data gatheringmodule 1002 may receive indications of whether signals received fromwearable photoplethysmography devices 100 are valid or invalid,according to whether or not the wearable photoplethysmography devices100 are correctly positioned in operation. Specifically, the validityclassifier builder data gathering module 1002 may receive indicationsspecifying that signals received from a specific wearablephotoplethysmography device 100 are generated when the device is eitherpositioned correctly in operation, being worn but not positionedcorrectly, or not being worn at all.

For example, the validity classifier builder data gathering module 1002may receive input indicating signal validity data specifying a wearablephotoplethysmography device 100 providing signals is positioned properlyon a wrist, finger, or thumb of a user, while the user is stationary orwalking on a treadmill. In another example, the validity classifierbuilder data gathering module 1002 may receive input indicating signalinvalidity data specifying a wearable photoplethysmography device 100providing signals is located indoors, outdoors, in a dark environment,in a light environment, while a user is walking, tapping, covering, oruncovering energy receivers or transmitters of the device, or the useris facing energy emission sources (e.g. LCD videos).

In various implementations, the validity classifier builder datagathering module 1002 may receive alternating invalid and valid signalsfrom a wearable photoplethysmography device 100. For example, thevalidity classifier builder data gathering module 1002 may receiveinvalid signals for a minute and receive valid signals for a minute inan alternating fashion.

In various implementations, the validity classifier builder datagathering module 1002 may build valid and invalid data samplecollections based on signals received from wearable photoplethysmographydevices 100. Valid and invalid data sample collections include samplesof known invalid and valid signals within specific signal windows. Forexample, the validity classifier builder data gathering module 1002 maybuild valid and invalid data sample collections including samples of 8second signal windows of known valid and invalid signals received fromwearable photoplethysmography devices 100. In another example, thevalidity classifier builder data gathering module 1002 may build validand invalid data sample collections that in combination contain over alarge number of (e.g., 12,000) sampled of valid and invalid signalsreceived from wearable photoplethysmography devices 100.

The wave metric calculation selection module 1004 selects wave metriccalculations. Wave metric calculations selected by the wave metriccalculation selection module 1004 may be determined from signalsreceived by the validity classifier builder data gathering module 1002for use in building a validity classifier. The metric wave calculationselection module 1004 may select wave metric calculations from the listof wave metric calculations as discussed with respect to FIG. 1. Forexample, the wave metric calculation selection module 1004 may select aset of wave metric calculations to determine from the signals receivedby the validity classifier builder data gathering module 1002 includingsignal energy measurements, signal mobility measurements, signalcomplexity measurements, signal crossing rate measurements, and signalnon-oscillatory component measurements. Further in the example, the wavecalculation selection module 1004 selects the set of wave metriccalculations because they may be calculated in a time directlyproportional to a length of a received signal and/or a validityclassifier created from the calculations may yield a simple polynomialfor validity prediction.

In one implementation, the wave metric calculation selection module 1004may systematically add or remove wave metric calculations to or fromselected wave metric calculations. The wave metric calculation selectionmodule 1004 may systematically add or remove wave metric calculations toor from selected wave metric calculations based on accuracy performancein determining validity of a signal using a generated validityclassifier. Additionally, the wave metric calculation selection module1004 may systematically add or remove wave metric calculations to orform selected wave metric calculations based on computational costsassociated with determining the wave metric calculations.

The validity classifier training module 1006 determines wave metriccalculations for signals received by the validity classifier builderdata gathering module. For example, the validity classifier trainingmodule 1006 may determine wave metric calculations for any number of thevalid and invalid data samples of the valid and invalid data samplecollections generated by the validity classifier builder data gatheringmodule 1002. The validity classifier training module 1006 may determinea set of wave metric calculations as selected by the wave metriccalculation selection module 1004. For example, the validity classifiertraining module 1006 may determine signal energy measurements, signalmobility measurements, signal complexity measurements, signal crossingrate measurements, and signal non-oscillatory component measurements forany number of the valid and invalid data samples in the valid andinvalid data sample collections. Further in the example, the validityclassifier training module 1006 may determine the measurements accordingto the techniques described with reference to the wave metriccalculation determination module 800 (e.g. as illustrated by ExampleEquations 1-4).

The validity classifier training module 1006 builds a validityclassifier by training the validity classifier. In training a validityclassifier, the validity classifier training module may analyze patternsof wave metric calculations of known valid and invalid signals. Inanalyzing patters of wave metric calculations in building a validityclassifier, the validity classifier training module 1006 may apply oneor a combination of applicable machine learning algorithms to the wavemetric calculations determined by the validity classifier trainingmodule 1006. In applying applicable machine learning algorithms orpattern recognitions algorithms to the wave metric calculations, thevalidity classifier training module 1006 may segment determined wavemetric calculations of known valid and invalid signals into clusters ofwave metric calculations of known valid and invalid signals. Further,the validity classifier training module 1006 may generate and segmentvalidity scores generated from determined wave metric calculations andassociated with known valid and invalid signals into clusters ofvalidity scores of known valid and invalid signals. Example algorithmsthe validity classifier training module 1006 may apply in building thevalidity classifier include kernel method algorithms, non-linear kernelmethod algorithms, polynomial kernel method algorithms, Gaussiankernels, or radial-bases kernels.

In various embodiments, the validity classifier training module 1006 mayapply a quadratic kernel in segmenting wave metric calculations intoclusters of wave metric calculations of known valid and invalid signals.Example Equation 5, shown below, illustrates an example of a quadratickernel equation the validity classifier training module 1006 may use inbuilding the validity classifier.K(x ₁ ,x ₂)=(x ₁ ^(T) x ₂+1)^(p), where p=2  Example Equation 5

In Example Equation 5, x1 and x2 are vectors representing the determinedwave metric calculations of known valid and invalid signals. The kernelequation shown in Example Equation 5 may be used to process a dotproduct of vectors representing the determined wave metric calculationsin a high dimensional space without explicitly making the combination ofthe vectors. For example, If x₁=(a, b) and x₂=(c, d) in the kernelequation above, the output is K(x₁,x₂)=1+a²b²+2abcd+c²d²+2ab+2cd, whichmay also be broken into a dot product between vectors containing variouspowers and coefficients on the variables, (1, a², √{square root over(2)}ab, b², √{square root over (2)}a, √{square root over (2)}b)·(1, c²,√{square root over (2)}cd, d², √{square root over (2)}c, √{square rootover (2)}d).

In various embodiments, the output of the application of an applicablemachine learning algorithm to the determined wave metric calculations(e.g. Example Equation 5), is substituted into an applicable supportvector machine. Example Equation 6, shown below, illustrates an examplesupport vector machine.

$\begin{matrix}{{F(x)} = {{sign}\left( {{\sum\limits_{i}{a_{i}y_{i}{K\left( {x_{i},x} \right)}}} + b} \right)}} & {{Example}\mspace{14mu}{Equation}\mspace{14mu} 6}\end{matrix}$

In Example Equation 6, K(x_(i),x) is the output of the kernel equationapplied to the determined wave metric calculations, where x_(i) mayserve as the training input (e.g. wave metric calculations of knownvalid and invalid signals, and x may serve as unlabeled input). Asupport vector machine forms the basis of the validity classifier and isused in generating, at least in part, validity scores for signals ofunknown validity. In various embodiments, determined wave metriccalculations may be input into the support vector machine to determine avalidity score for a signal of unknown validity. For example, the signalvalidity classification system or may input determined wave metriccalculations into the support vector machine generated from applicationof an applicable machine learning algorithm to wave metric calculationsof known valid and invalid signals, to determine whether a signal ofunknown validity is actually valid. With reference to the ExampleEquation 6, x serves as an unlabeled input in which determined wavemetric calculations from signals of unknown validity may be input tocalculate, at least in part, a validity score for the signals.

The validity classifier tuning module 1008 tunes the validity classifiertrained, or otherwise generated, by the validity classifier trainingmodule 1006. In various embodiments, in tuning a validity classifiertrained by the validity classifier training module 1006, the validityclassifier tuning module 1008 may fit the classifier to a logisticfunction. For example, validity classifier tuning module 1008 may fit asupport vector machine used to generate validity scores for signalsbased on determined wave metric calculations to a logistic function.Example Equation 7, shown below, illustrates an example of a classifierfit to a logistic functions.

$\begin{matrix}{{{Valid}\mspace{14mu}{Probability}\mspace{14mu}({score})} = \frac{1}{1 + e^{{{- a}*{score}} - b}}} & {{Example}\mspace{14mu}{Equation}\mspace{14mu} 7}\end{matrix}$

In Example Equation 6, the score may be generated through application ofwave metric calculations to a support vector machine (e.g., as shown inExample Equation 6), as part of generating a validity score for a signalthrough application of the validity classifier. In Example Equation 7, aand b serve as fit parameters and are used to fit output validity scoresgenerated through application Equation 7 within one or a plurality ofspecific ranges.

In various embodiments, the validity classifier tuning module 1008 mayset a validity threshold, included as part of or otherwise associatedwith the validity classifier. The validity classifier tuning module 1008may set a validity threshold for the validity classifier to achieve aspecific number of true positive results for determining signalvalidity. For example, the validity classifier tuning module 1008 mayset a validity threshold to achieve a true positive result determinationrate of 99%. Additionally, the validity classifier tuning module 1008may set a validity threshold of a validity classifier specific to and/orbased on one or a combination of a device type of a photoplethysmographydevice 100 using the validity classifier, medical signs being measuredby a photoplethysmography device 100 using the validity classifier, andcharacteristics of a user of a photoplethysmography device 100 using thevalidity classifier. For example, if clusters of valid and invalidvalidity scores, determined through application of the validityclassifier, differ for different types of photoplethysmography devices100, then the validity classifier tuning module 1008 may set twodifferent validity thresholds to use dependent on which device type isbeing used.

The validity classifier datastore 1010 stores validity classifier dataindicating a validity classifier generated by the validity classifiertraining module 1006 and/or tuned by the validity classifier tuningmodule 1008. For example, validity classifier data stored in thevalidity classifier datastore 1010 may include a support vector machinetrained using wave metric calculations of known valid and invalidsignals and fit to a logistic function to generate validity scoresfalling within one or a plurality of specific ranges. Additionally,validity classifier data stored in the validity classifier datastore1010 may include one or a validity of thresholds to use in determiningwhether signals are valid through application of validity classifiers.In various embodiments, the validity classifier datastore 1010 mayinclude indications of when to use specific validity thresholds. Forexample, validity classifier data stored in the validity classifierdatastore 1010 may indicate which validity thresholds to use to achievea specific number of true positive results, and which validitythresholds to use based on device type of photoplethysmography devices100, user characteristics of users of photoplethysmography devices 100,and medical signs measured by photoplethysmography devices 100. Avalidity threshold used in application of the validity classifier may beadjusted to one of a plurality of validity thresholds using validityclassifier data stored in the validity classifier datastore 1010.

It will be understood that for some embodiments, the modules anddatastores or the arrangement of modules and datastores may differ fromwhat is depicted in FIG. 10.

Each of the modules and datastore of the validity classifier buildersystem 1000 may be implemented using one or more digital devices. Anexample digital device is described regarding FIG. 5.

FIG. 11 depicts an example flow diagram of a method 1100 of generating avalidity classifier for purposes of determining whether a wearablephotoplethysmography device 100 is correctly positioned in operating tomeasure medical signs of a user. At step 1102, data used in building avalidity classifier including known valid and invalid signal isgathered. Data gathered at step 1102 includes signals generated by awearable photoplethysmography device in operating to measure medicalsigns of a user. Data gathered at step 1102 may be gathered by avalidity classifier builder data gathering module 1002. Data gathered atstep 1102 may include signal validity data indicating whether a wearablephotoplethysmography device 100 is correctly positioned. Additionally,data gathered at step 1102 may include signal invalidity data indicatinga wearable photoplethysmography device is incorrectly positioned oroperating in an environment leading to receiving invalid signal.

At step 1104, wave metric calculations are selected to determine fromthe known valid and invalid signals. Wave metric calculations may beselected by the wave metric calculation selection module 1004. Potentialwave metric calculations capable of being selected include the wavemetric calculation in the list of wave metric calculations as discussedwith respect to FIG. 1. For example, set of wave metric calculationsincluding signal energy measurements, signal mobility measurements,signal complexity measurements, signal crossing rate measurements, andsignal non-oscillatory component measurements may be selected. Wavemetric calculations may be selected based on either or both an accuracyperformance in determining validity of signals using the wave metriccalculations and computational costs of determining the wave metriccalculations.

At step 1106, a validity classifier is trained using the selected wavemetric calculations. The validity classifier training module 1006 maytrain a validity classifier using the selected wave metric calculations.In training a validity classifier an applicable machine learningalgorithm may be applied wave metric calculations of the selected wavemetric calculations determined from the known valid and invalid signals.For example, a kernel equation may be applied to wave metriccalculations of the selected wave metric calculations. Additionally,output of applying a machine learning algorithm to wave metriccalculations of the known valid and invalid signals may be applied to asupport vector machine as training input to form the validityclassifier. For example, output of a kernel equation applied to wavemetric calculations of the selected wave metric calculations may beapplied as training input to a support vector machine.

At step 1108, the validity classifier is tuned to produce validityscores within one or a plurality of specific ranges. The validityclassifier tuning module 1008 may tune to the classifier to producevalidity scores within one or a plurality of specific ranges. Forexample, a logistic function may be fit to the validity classifier tocause scores generated through application of the validity classifier tofall within one or a plurality of specific ranges.

At step 1108, in various embodiments, in tuning the validity classifier,one or a plurality of validity thresholds are determined for thevalidity classifier. The validity classifier tuning module 1008 mayselect one or a plurality of validity thresholds as part of tuning thevalidity classifier. Validity thresholds may be selected to achieve aspecific true positive result determination rate. For example a validitythreshold may be selected to achieve a 99% true positive resultdetermination rate. Additionally, validity thresholds may be selectedaccording to one or a combination of device type of photoplethysmographydevices 100, user characteristics of users of photoplethysmographydevices 100, and medical signs measured by photoplethysmography devices100.

The present invention(s) are described above with reference to exampleembodiments. It will be apparent to those skilled in the art thatvarious modifications may be made and other embodiments may be usedwithout departing from the broader scope of the present invention(s).Therefore, these and other variations upon the example embodiments areintended to be covered by the present invention(s).

The invention claimed is:
 1. A method comprising: generating a signalfrom an energy receiver of a wearable photoplethysmography device, thesignal generated by the energy receiver, at least in part, being basedupon at least a portion of energy that was initially projected by anenergy transmitter of the wearable photoplethysmography device operatingto measure medical signs of a user; selecting a subset of wave featuresof a plurality of wave features of the signal; determining wave metriccalculations of a set of wave metric calculations for the subset of wavefeatures; classifying the signal by applying a validity classifier to atleast one of the wave metric calculations to generate a validity score,the validity classifier generated based on the set of wave metriccalculations and used to determine if the wearable photoplethysmographydevice is correctly positioned on the user; comparing the validity scoreto a validity threshold to determine if the wearablephotoplethysmography device is correctly positioned; controllingoperation of the wearable photoplethysmography device based on whetherit is determined the wearable photoplethysmography device is correctlypositioned based on a comparison of the validity score to the validitythreshold; classifying the signal by applying the validity classifier tothe wave metric calculations of the set of wave metric calculations togenerate a plurality of validity scores including the validity score forthe signal; applying temporal smoothing to the plurality of validityscores for the signal to generate a smoothed validity score; comparingthe smoothed validity score to the validity threshold to determine ifthe wearable photoplethysmography device is correctly positioned inoperating to measure the medical signs of the user; and controllingoperation of the wearable photoplethysmography device based on whetherit is determined the wearable photoplethysmography device is correctlypositioned in operating to measure the medical signs of the user basedon a comparison of the smoothed validity score to the validitythreshold.
 2. The method of claim 1, wherein the wave metriccalculations of the set of wave metric calculations include signalenergy measurements of the signal determined from the subset of wavefeatures.
 3. The method of claim 2, wherein the wave metric calculationsof the set of wave metric calculations include signal mobilitymeasurements of the signal determined from the subset of wave featuresand the signal energy measurements of the signal determined from thesubset of wave features.
 4. The method of claim 3, wherein the wavemetric calculations of the set of wave metric calculations includesignal complexity measurements of the signal determined from the subsetof wave features and the signal mobility of the measurements of thesignal determined from the subset of wave features.
 5. The method ofclaim 1, wherein the wave metric calculations of the set of wave metriccalculations include one or a combination of signal crossingmeasurements of the signal and signal non-oscillatory componentmeasurements of the signal determined from the subset of wave features.6. The method of claim 1, wherein the validity classifier is generatedfrom one or a combination of signal energy measurements, signal mobilitymeasurements, signal complexity measurements, signal crossingmeasurements, and signal non-oscillatory component measurements ofsignals known to be valid or invalid by being generated by at least onewearable photoplethysmography device correctly positioned in operatingto measure the medical signs and at least another wearablephotoplethysmography device incorrectly positioned in operating tomeasure the medical signs.
 7. The method of claim 1, further comprising:determining the wearable photoplethysmography device is operating tomeasure the medical signs of the user based on the comparison of thevalidity score to the validity threshold; causing the energy transmitterto stop operating in emitting energy for use by the wearablephotoplethysmography device in operating to measure the medical signs ofthe user.
 8. The method of claim 1, wherein the temporal smoothingincludes applying rank smoothing to the plurality of validity scores,the smoothed validity score selected from the plurality of validityscores based on the value of the smoothed validity score according tothe rank smoothing.
 9. The method of claim 1, further comprisingnormalizing the signal before selecting the subset of wave features ofthe plurality of wave features of the signal.